## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr
## Conflicts with tidy packages ----------------------------------------------
## filter(): dplyr, stats
## lag(): dplyr, stats
## Loading 'meta' package (version 4.8-2).
## Type 'help(meta)' for a brief overview.
Searches were done using Web of Science.
searchTerms <- read_csv("search_terms.csv")
## Parsed with column specification:
## cols(
## search = col_integer(),
## terms = col_character(),
## hits = col_integer()
## )
datatable(searchTerms)
prisma(found = 118,
found_other = 11,
no_dupes = 113,
screened = 113,
screen_exclusions = 87,
full_text = 26,
full_text_exclusions = 14,
qualitative = 12,
quantitative = 0,
width = 800, height = 800)
## Loading required namespace: DiagrammeR
paperReasons <- read_csv("paper_reasons.csv")
## Parsed with column specification:
## cols(
## Reason = col_character(),
## Papers = col_integer()
## )
datatable(paperReasons)
Histogram of the reasons for excluding papers:
ggplot(paperReasons, aes(Reason, Papers)) + geom_bar(stat = "identity") + coord_flip()
data <- read_csv("meta-analysis_data.csv")
## Parsed with column specification:
## cols(
## Paper_ID = col_integer(),
## Reviewer = col_character(),
## StudyType = col_character(),
## Age_class = col_character(),
## Start_time = col_integer(),
## End_time = col_integer(),
## Season = col_character(),
## Driver = col_character(),
## Response_Units = col_character(),
## EffectSize_d = col_double()
## )
datatable(data)
A range of drivers were investigated:
data.simple <- data %>% group_by(Driver) %>% count()
ggplot(na.omit(data.simple), aes(Driver, n)) + geom_bar(stat = "identity") + coord_flip()
Three main study types were represented:
data.simple <- data %>% group_by(StudyType) %>% count()
ggplot(na.omit(data.simple), aes(StudyType, n)) + geom_bar(stat = "identity") + coord_flip()
Age classes and definitions varied:
data$Age_class[grep('juvenile',data$Age_class)] <- 'Juvenile'
data.simple <- data %>% group_by(Age_class) %>% count()
ggplot(na.omit(data.simple), aes(Age_class, n)) + geom_bar(stat = "identity") + coord_flip()
treedata <- data %>% group_by(Driver) %>% summarise(mean.EffectSize_d = mean(EffectSize_d),
error = std.error(EffectSize_d))
m <- metagen(mean.EffectSize_d, error, studlab = Driver, data = treedata) #fit generic meta-analysis to an object
#viz (draw a standard forest plot or metaregression plot)
forest(m) #grid-based graphics so a bit of work to resize